Job Description
We are not just building software; we are architecting the future. As we look toward 2026, the definition of Artificial Intelligence is shifting from static models to autonomous, agentic systems. Apex Innovation Labs is seeking a visionary Senior AI Architect to lead our R&D division in defining the next generation of intelligent infrastructure.
In this role, you will bridge the gap between theoretical breakthroughs and production-ready systems. You will be responsible for designing scalable, fault-tolerant architectures that integrate Generative AI, Neural Architecture Search (NAS), and Edge Computing. If you are passionate about solving complex problems and want to be at the forefront of the technology stack that will define 2026, we want to meet you.
Why Join Us?
- Work on cutting-edge projects with a focus on AI Orchestration and Autonomous Agents.
- Competitive compensation package with equity options.
- Flexible remote-first culture with premium co-working spaces in SF.
- Access to the latest hardware for model training and inference.
Responsibilities
- Architect end-to-end AI pipelines capable of handling petabyte-scale data streams, optimized for the high-latency demands of 2026 applications.
- Lead the design and implementation of Agentic AI frameworks that can autonomously decompose and solve complex multi-step problems.
- Collaborate with cross-functional teams to integrate Neural Interface technologies into consumer and enterprise software.
- Define technical roadmaps and best practices for model deployment, ensuring scalability, security, and ethical AI compliance.
- Mentor junior engineers and data scientists, fostering a culture of continuous innovation and technical excellence.
- Conduct rigorous research into emerging paradigms such as Spatial Computing and Quantum-AI Hybrid models.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related field (PhD preferred).
- 10+ years of experience in software architecture, with at least 5 years focused on Machine Learning and Deep Learning systems.
- Deep expertise in Python, PyTorch, and TensorFlow frameworks.
- Proven track record of deploying Large Language Models (LLMs) and Multimodal systems at scale.
- Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization technologies (Kubernetes, Docker).
- Strong understanding of distributed systems, microservices, and high-availability architecture.
- Excellent communication skills with the ability to translate complex technical concepts into business value.